{"id":"https://openalex.org/W4380628792","doi":"https://doi.org/10.1145/3585542.3585551","title":"A new algorithm for source enumeration in large dimensional regime","display_name":"A new algorithm for source enumeration in large dimensional regime","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4380628792","doi":"https://doi.org/10.1145/3585542.3585551"},"language":"en","primary_location":{"id":"doi:10.1145/3585542.3585551","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3585542.3585551","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Digital Signal Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049166570","display_name":"Shoucheng Yuan","orcid":"https://orcid.org/0000-0002-3634-3254"},"institutions":[{"id":"https://openalex.org/I4210139598","display_name":"Puer University","ror":"https://ror.org/02zvhxb95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210139598"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shoucheng Yuan","raw_affiliation_strings":["College of Mathematics and Statistics, Puer University, China"],"raw_orcid":"https://orcid.org/0000-0002-3634-3254","affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Puer University, China","institution_ids":["https://openalex.org/I4210139598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069785520","display_name":"Bin Zhang","orcid":"https://orcid.org/0000-0001-7654-5072"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Zhang","raw_affiliation_strings":["College of Mathematics and Statistics, Guangxi Normal University, China"],"raw_orcid":"https://orcid.org/0000-0001-7654-5072","affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Guangxi Normal University, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101795195","display_name":"Junjie Wang","orcid":"https://orcid.org/0009-0005-6039-0199"},"institutions":[{"id":"https://openalex.org/I4210139598","display_name":"Puer University","ror":"https://ror.org/02zvhxb95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210139598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Wang","raw_affiliation_strings":["College of Mathematics and Statistics, Puer University, China"],"raw_orcid":"https://orcid.org/0000-0002-1583-4775","affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Puer University, China","institution_ids":["https://openalex.org/I4210139598"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049166570"],"corresponding_institution_ids":["https://openalex.org/I4210139598"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05626741,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"58","last_page":"63"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.7357393503189087},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.7109698057174683},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6438091993331909},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.6002242565155029},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.5950675010681152},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.590231716632843},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.588179886341095},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.5401090979576111},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5061551928520203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.49674469232559204},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49021226167678833},{"id":"https://openalex.org/keywords/enumeration","display_name":"Enumeration","score":0.4637589454650879},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.41432806849479675},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4134218990802765},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3268994092941284},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.3065120577812195},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.12421420216560364},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06813961267471313}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7357393503189087},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.7109698057174683},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6438091993331909},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.6002242565155029},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5950675010681152},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.590231716632843},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.588179886341095},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.5401090979576111},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5061551928520203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49674469232559204},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49021226167678833},{"id":"https://openalex.org/C156340839","wikidata":"https://www.wikidata.org/wiki/Q2704791","display_name":"Enumeration","level":2,"score":0.4637589454650879},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.41432806849479675},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4134218990802765},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3268994092941284},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3065120577812195},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.12421420216560364},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06813961267471313},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3585542.3585551","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3585542.3585551","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Digital Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1491289695","https://openalex.org/W1966950301","https://openalex.org/W2009506162","https://openalex.org/W2048464858","https://openalex.org/W2054658115","https://openalex.org/W2099551908","https://openalex.org/W2123226256","https://openalex.org/W2140262081","https://openalex.org/W2142635246","https://openalex.org/W2148744371","https://openalex.org/W2152687999","https://openalex.org/W2153618310","https://openalex.org/W2155421090","https://openalex.org/W2170852717","https://openalex.org/W2340413290","https://openalex.org/W2341033461","https://openalex.org/W2611023048","https://openalex.org/W3172092805"],"related_works":["https://openalex.org/W3015822731","https://openalex.org/W4386762140","https://openalex.org/W2397904024","https://openalex.org/W2075332338","https://openalex.org/W1970221037","https://openalex.org/W4386776152","https://openalex.org/W1826709224","https://openalex.org/W2135903028","https://openalex.org/W2488034322","https://openalex.org/W2913739054"],"abstract_inverted_index":{"This":[0],"article":[1],"proposes":[2],"a":[3,82],"method":[4,20,80],"for":[5,18,65],"estimating":[6],"the":[7,19,25,31,37,44,53,60,63,67,78,89,92],"number":[8,68],"of":[9,43,48,55,69,85],"signals":[10,71],"with":[11],"high-dimensional":[12],"low":[13],"sample":[14,32,49],"size.":[15],"The":[16,39],"inspiration":[17],"comes":[21],"from":[22],"testing":[23],"whether":[24],"covariance":[26],"matrix":[27],"is":[28,34,72],"spherical":[29],"when":[30],"size":[33],"less":[35],"than":[36,98],"dimension.":[38],"test":[40],"statistic":[41],"consists":[42],"first":[45],"four":[46],"moments":[47],"eigenvalues":[50],"and":[51,91,95],"relaxes":[52],"assumption":[54],"Gaussian":[56,90],"distribution.":[57],"Based":[58],"on":[59],"generalized":[61],"BIC,":[62],"expression":[64],"determining":[66],"source":[70],"given.":[73],"Simulation":[74],"results":[75],"demonstrate":[76],"that":[77],"proposed":[79],"has":[81],"high":[83],"probability":[84],"detection":[86],"in":[87],"both":[88],"non-Gaussian":[93],"noises":[94],"performs":[96],"better":[97],"some":[99],"existing":[100],"methods.":[101]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
